Map cumulative impacts by stressor group

knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE, fig.height = 4, fig.width = 7)

library(terra)
library(oharac)
library(tidyverse)
library(here)
source(here('common_fxns.R'))

1 Summary

With impact maps generated for all stressors, means calculated both species and FE, we can add the impact layers to generate a comprehensive cumulative human impact map. This script aggregates by stressor group: fishing-related, land-based, ocean-based, and climate.

2 Data

See scripts 2, 3a, and 3b for vulnerability mapping scripts, and scripts 4a-4d for impact calculation scripts.

3 Methods

3.1 Calculate CHI: species by stressor group

Sum impacts across all stressor/vulnerability combos, by stressor group.

str_gp_df <- read_csv(here('_raw/stressor_vulnerability_lookup.csv'))

imp1_fs <- list.files(here_anx('_output/impact_maps/impact_maps_species'),
                      pattern = 'mean',
                      full.names = TRUE)
imp1_stack <- rast(imp1_fs) %>%
  setNames(str_remove_all(basename(imp1_fs), '.+_spp_|_x_.+|_mean.tif'))

ocean_a_rast <- rast(here('_spatial/ocean_area_mol.tif'))

spp_outfile_stem <- here('_output/cumulative_impact_maps/species_group_chi_%s.tif')

str_gps <- str_gp_df$stressor_group %>% unique()
for(str_gp in str_gps) {
  ### str_gp <- str_gps[1]
  spp_outfile <- sprintf(spp_outfile_stem, str_gp)
  
  if(!file.exists(spp_outfile)) {
    str_vec <- str_gp_df %>% 
      filter(stressor_group == str_gp) %>% 
      .$stressor
    
    gp_stack <- imp1_stack[[str_vec]]
    ### Calculate cumulative impacts for this group
    gp_chi_spp_raw <- sum(gp_stack, na.rm = TRUE)
    gp_chi_species <- gp_chi_spp_raw %>%
      mask(ocean_a_rast) %>%
      setNames(sprintf('%s_chi_spp', str_gp))
    
    writeRaster(gp_chi_species, spp_outfile,
                overwrite = TRUE)
  }
}

for(s in str_gps) {
  ### s <- str_gps[1]
  f <- sprintf(spp_outfile_stem, s)
  r <- rast(f)
  plot(log10(r), col = hcl.colors(50), 
       main = paste0('Log_10(', s, ' stressors), species'),
       axes = FALSE)
}

3.2 Calculate CHI: Functional Entity

Sum impacts across all stressor/vulnerability combos.

imp2_fs <- list.files(here_anx('_output/impact_maps/impact_maps_funct_entity'), 
                      pattern = 'mean',
                      full.names = TRUE)

imp2_stack <- rast(imp2_fs) %>%
  setNames(str_remove_all(basename(imp2_fs), '.+_fe_|_x_.+|_mean.tif'))

fe_outfile_stem <- here('_output/cumulative_impact_maps/funct_entity_group_chi_%s.tif')

str_gps <- str_gp_df$stressor_group %>% unique()
for(str_gp in str_gps) {
  ### str_gp <- str_gps[1]
  fe_outfile <- sprintf(fe_outfile_stem, str_gp)
  
  if(!file.exists(fe_outfile)) {
    str_vec <- str_gp_df %>% 
      filter(stressor_group == str_gp) %>% 
      .$stressor
    
    gp_stack <- imp2_stack[[str_detect(names(imp2_stack), smash2or(str_vec))]]
    ### Calculate cumulative impacts for this group
    gp_chi_fe_raw <- sum(gp_stack, na.rm = TRUE)
    gp_chi_fe <- gp_chi_fe_raw %>%
      mask(ocean_a_rast) %>%
      setNames(sprintf('%s_chi_fe', str_gp))
    
    writeRaster(gp_chi_fe, fe_outfile,
                overwrite = TRUE)
  }
}

for(s in str_gps) {
  ### s <- str_gps[1]
  f <- sprintf(fe_outfile_stem, s)
  r <- rast(f)
  plot(log10(r), col = hcl.colors(50), 
       main = paste0('Log_10(', s, ' stressors), FE'),
       axes = FALSE)
}

3.3 Plot relative difference in CHI

Difference in CHI calculated from FE vulnerability relative to species vulnerability (using only spp included in functional entities): \[\frac{CHI_{FE} - CHI_{spp}}{CHI_{spp}} \times 100\%\]

for(s in str_gps) {
  ### s <- str_gps[1]
  f1 <- sprintf(fe_outfile_stem, s)
  r1 <- rast(f1)
  f2 <- sprintf(spp_outfile_stem, s)
  r2 <- rast(f2)
  r <- (r1 - r2) / r2
  
  plot(r, col = hcl.colors(50), 
       main = paste0('Difference: FE vs species ', s, ' stressors'),
       axes = FALSE)
}